Merge branch 'nested_decision_exe' into nested_decision_exe

qlib/backtest/account.py

@@ -160,7 +160,7 @@ class Account:
             self.accum_info.add_return_value(profit)  # note here do not consider cost
 
     def update_order(self, order, trade_val, cost, trade_price):
-        if not self.is_port_metr_enabled():
+        if self.current.skip_update():
             # TODO: supporting polymorphism for account
             # updating order for infinite position is meaningless
             return

qlib/backtest/exchange.py

@@ -2,6 +2,7 @@
 # Licensed under the MIT License.
 
 
+from qlib.backtest.position import Position
 import random
 import logging
 from typing import List, Tuple, Union, Callable, Iterable
@@ -281,6 +282,8 @@ class Exchange:
         """
         Deal order when the actual transaction
 
+        the results section in `Order` will be changed.
+
         :param order:  Deal the order.
         :param trade_account: Trade account to be updated after dealing the order.
         :param position: position to be updated after dealing the order.
@@ -343,6 +346,7 @@ class Exchange:
             `None`: if the stock is suspended `None` may be returned
             `float`: return factor if the factor exists
         """
+        assert (start_time is not None and end_time is not None, "the time range must be given")
         if stock_id not in self.quote.get_all_stock():
             return None
         return self.quote.get_data(stock_id, start_time, end_time, fields="$factor", method=ts_data_last)
@@ -505,20 +509,56 @@ class Exchange:
                 )
         return value
 
-    def get_amount_of_trade_unit(self, factor):
+    def _get_factor_or_raise_erorr(self, factor: float = None, stock_id: str = None, start_time=None, end_time=None):
+        """Please refer to the docs of get_amount_of_trade_unit"""
+        if factor is None:
+            if stock_id is not None and start_time is not None and end_time is not None:
+                factor = self.get_factor(stock_id=stock_id, start_time=start_time, end_time=end_time)
+            else:
+                raise ValueError(f"`factor` and (`stock_id`, `start_time`, `end_time`) can't both be None")
+        return factor
+
+    def get_amount_of_trade_unit(self, factor: float = None, stock_id: str = None, start_time=None, end_time=None):
+        """
+        get the trade unit of amount based on **factor**
+
+        the factor can be given directly or calculated in given time range and stock id.
+        `factor` has higher priority than `stock_id`, `start_time` and `end_time`
+
+        Parameters
+        ----------
+        factor : float
+            the adjusted factor
+        stock_id : str
+            the id of the stock
+        start_time :
+            the start time of trading range
+        end_time :
+            the end time of trading range
+        """
         if not self.trade_w_adj_price and self.trade_unit is not None:
+            factor = self._get_factor_or_raise_erorr(
+                factor=factor, stock_id=stock_id, start_time=start_time, end_time=end_time
+            )
             return self.trade_unit / factor
         else:
             return None
 
-    def round_amount_by_trade_unit(self, deal_amount, factor):
+    def round_amount_by_trade_unit(
+        self, deal_amount, factor: float = None, stock_id: str = None, start_time=None, end_time=None
+    ):
         """Parameter
+        Please refer to the docs of get_amount_of_trade_unit
+
         deal_amount : float, adjusted amount
         factor : float, adjusted factor
         return : float, real amount
         """
         if not self.trade_w_adj_price and self.trade_unit is not None:
             # the minimal amount is 1. Add 0.1 for solving precision problem.
+            factor = self._get_factor_or_raise_erorr(
+                factor=factor, stock_id=stock_id, start_time=start_time, end_time=end_time
+            )
             return (deal_amount * factor + 0.1) // self.trade_unit * self.trade_unit / factor
         return deal_amount
 
@@ -529,7 +569,7 @@ class Exchange:
         else:
             return deal_amount
 
-    def _calc_trade_info_by_order(self, order, position):
+    def _calc_trade_info_by_order(self, order, position: Position):
         """
         Calculation of trade info
 
@@ -541,6 +581,7 @@ class Exchange:
         """
 
         trade_price = self.get_deal_price(order.stock_id, order.start_time, order.end_time, direction=order.direction)
+        order.factor = self.get_factor(order.stock_id, order.start_time, order.end_time)
         if order.direction == Order.SELL:
             # sell
             if position is not None:

qlib/backtest/executor.py

@@ -1,5 +1,6 @@
 from abc import abstractclassmethod, abstractmethod
 import copy
+from qlib.log import get_module_logger
 from types import GeneratorType
 from qlib.backtest.account import Account
 import warnings
@@ -102,7 +103,10 @@ class BaseExecutor:
         self.track_data = track_data
         self._trade_exchange = trade_exchange
         self.level_infra = LevelInfrastructure()
+        self.level_infra.reset_infra(common_infra=common_infra)
         self.reset(start_time=start_time, end_time=end_time, common_infra=common_infra)
+        if common_infra is None:
+            get_module_logger("BaseExecutor").warning(f"`common_infra` is not set for {self}")
 
     def reset_common_infra(self, common_infra):
         """
@@ -239,7 +243,7 @@ class BaseExecutor:
             # Some concrete executor don't have inner decisions
             res, kwargs = obj
 
-        trade_start_time, trade_end_time = self.trade_calendar.get_cur_step_time()
+        trade_start_time, trade_end_time = self.trade_calendar.get_step_time()
         # Account will not be changed in this function
         self.trade_account.update_bar_end(
             trade_start_time,
@@ -332,7 +336,7 @@ class NestedExecutor(BaseExecutor):
         self.inner_strategy.reset_common_infra(common_infra)
 
     def _init_sub_trading(self, trade_decision):
-        trade_start_time, trade_end_time = self.trade_calendar.get_cur_step_time()
+        trade_start_time, trade_end_time = self.trade_calendar.get_step_time()
         self.inner_executor.reset(start_time=trade_start_time, end_time=trade_end_time)
         sub_level_infra = self.inner_executor.get_level_infra()
         self.level_infra.set_sub_level_infra(sub_level_infra)
@@ -379,8 +383,8 @@ class NestedExecutor(BaseExecutor):
                 )
                 trade_decision.mod_inner_decision(_inner_trade_decision)  # propagate part of decision information
 
-                # NOTE sub_cal.get_cur_step_time() must be called before collect_data in case of step shifting
-                decision_list.append((_inner_trade_decision, *sub_cal.get_cur_step_time()))
+                # NOTE sub_cal.get_step_time() must be called before collect_data in case of step shifting
+                decision_list.append((_inner_trade_decision, *sub_cal.get_step_time()))
 
                 # NOTE: Trade Calendar will step forward in the follow line
                 _inner_execute_result = yield from self.inner_executor.collect_data(
@@ -478,7 +482,7 @@ class SimulatorExecutor(BaseExecutor):
 
     def _collect_data(self, trade_decision: BaseTradeDecision, level: int = 0):
 
-        trade_start_time, _ = self.trade_calendar.get_cur_step_time()
+        trade_start_time, _ = self.trade_calendar.get_step_time()
         execute_result = []
 
         for order in self._get_order_iterator(trade_decision):
@@ -491,30 +495,22 @@ class SimulatorExecutor(BaseExecutor):
                 execute_result.append((order, trade_val, trade_cost, trade_price))
                 if self.verbose:
                     if order.direction == Order.SELL:  # sell
-                        print(
-                            "[I {:%Y-%m-%d %H:%M:%S}]: sell {}, price {:.2f}, amount {}, deal_amount {}, factor {}, value {:.2f}.".format(
-                                trade_start_time,
-                                order.stock_id,
-                                trade_price,
-                                order.amount,
-                                order.deal_amount,
-                                order.factor,
-                                trade_val,
-                            )
-                        )
+                        action = "sell"
                     else:
-                        print(
-                            "[I {:%Y-%m-%d %H:%M:%S}]: buy {}, price {:.2f}, amount {}, deal_amount {}, factor {}, value {:.2f}.".format(
-                                trade_start_time,
-                                order.stock_id,
-                                trade_price,
-                                order.amount,
-                                order.deal_amount,
-                                order.factor,
-                                trade_val,
-                            )
+                        action = "buy"
+                    print(
+                        "[I {:%Y-%m-%d %H:%M:%S}]: {} {}, price {:.2f}, amount {}, deal_amount {}, factor {}, value {:.2f}, cach {:.2f}.".format(
+                            trade_start_time,
+                            action,
+                            order.stock_id,
+                            trade_price,
+                            order.amount,
+                            order.deal_amount,
+                            order.factor,
+                            trade_val,
+                            self.trade_account.get_cash(),
                         )
-
+                    )
             else:
                 if self.verbose:
                     print("[W {:%Y-%m-%d %H:%M:%S}]: {} wrong.".format(trade_start_time, order.stock_id))

qlib/backtest/order.py

@@ -3,7 +3,8 @@
 # TODO: rename it with decision.py
 from __future__ import annotations
 from enum import IntEnum
-from qlib.utils.time import concat_date_time
+from qlib.data.data import Cal
+from qlib.utils.time import concat_date_time, epsilon_change
 from qlib.log import get_module_logger
 
 # try to fix circular imports when enabling type hints
@@ -41,16 +42,24 @@ class Order:
             presents the weight factor assigned in Exchange()
     """
 
+    # 1) time invariant values
+    # - they are set by users and is time-invariant.
     stock_id: str
-    amount: float  # `amount` is a non-negative value
+    amount: float  # `amount` is a non-negative and adjusted value
+    direction: int
 
+    # 2) time variant values:
+    # - Users may want to set these values when using lower level APIs
+    # - If users don't, TradeDecisionWO will help users to set them
     # The interval of the order which belongs to (NOTE: this is not the expected order dealing range time)
     start_time: pd.Timestamp
     end_time: pd.Timestamp
 
-    direction: int
-    factor: float
+    # 3) results
+    # - users should not care about these values
+    # - they are set by the backtest system after finishing the results.
     deal_amount: float = field(init=False)  # `deal_amount` is a non-negative value
+    factor: float = field(init=False)
 
     # FIXME:
     # for compatible now.
@@ -127,8 +136,8 @@ class OrderHelper:
         code: str,
         amount: float,
         direction: OrderDir,
-        start_time: Union[str, pd.Timestamp],
-        end_time: Union[str, pd.Timestamp],
+        start_time: Union[str, pd.Timestamp] = None,
+        end_time: Union[str, pd.Timestamp] = None,
     ) -> Order:
         """
         help to create a order
@@ -143,9 +152,9 @@ class OrderHelper:
             **adjusted trading amount**
         direction : OrderDir
             trading  direction
-        start_time : Union[str, pd.Timestamp]
+        start_time : Union[str, pd.Timestamp] (optional)
             The interval of the order which belongs to
-        end_time : Union[str, pd.Timestamp]
+        end_time : Union[str, pd.Timestamp] (optional)
             The interval of the order which belongs to
 
         Returns
@@ -153,15 +162,17 @@ class OrderHelper:
         Order:
             The created order
         """
-        start_time = pd.Timestamp(start_time)
-        end_time = pd.Timestamp(end_time)
+        if start_time is not None:
+            start_time = pd.Timestamp(start_time)
+        if end_time is not None:
+            end_time = pd.Timestamp(end_time)
+        # NOTE: factor is a value belongs to the results section. User don't have to care about it when creating orders
         return Order(
             stock_id=code,
             amount=amount,
             start_time=start_time,
             end_time=end_time,
             direction=direction,
-            factor=self.exchange.get_factor(code, start_time, end_time),
         )
 
 
@@ -291,6 +302,7 @@ class BaseTradeDecision:
 
         """
         self.strategy = strategy
+        self.start_time, self.end_time = strategy.trade_calendar.get_step_time()
         self.total_step = None  # upper strategy has no knowledge about the sub executor before `_init_sub_trading`
         if isinstance(trade_range, Tuple):
             # for Tuple[int, int]
@@ -406,6 +418,62 @@ class BaseTradeDecision:
                 _start_idx, _end_idx = max(0, _start_idx), min(self.total_step - 1, _end_idx)
         return _start_idx, _end_idx
 
+    def get_data_cal_range_limit(self, rtype: str = "full", raise_error: bool = False) -> Tuple[int, int]:
+        """
+        get the range limit based on data calendar
+
+        NOTE: it is **total** range limit instead of a single step
+
+        The following assumptions are made
+        1) The frequency of the exchange in common_infra is the same as the data calendar
+        2) Users want the index mod by **day** (i.e. 240 min)
+
+        Parameters
+        ----------
+        rtype: str
+            - "full": return the full limitation of the deicsion in the day
+            - "step": return the limitation of current step
+
+        raise_error: bool
+            True: raise error if no trade_range is set
+            False: return full trade calendar.
+
+            It is useful in following cases
+            - users want to follow the order specific trading time range when decision level trade range is not
+              available. Raising NotImplementedError to indicates that range limit is not available
+
+        Returns
+        -------
+        Tuple[int, int]:
+            the range limit in data calendar
+
+        Raises
+        ------
+        NotImplementedError:
+            If the following criteria meet
+            1) the decision can't provide a unified start and end
+            2) raise_error is True
+        """
+        # potential performance issue
+        day_start = pd.Timestamp(self.start_time.date())
+        day_end = epsilon_change(day_start + pd.Timedelta(days=1))
+        freq = self.strategy.trade_exchange.freq
+        _, _, day_start_idx, day_end_idx = Cal.locate_index(day_start, day_end, freq=freq)
+        if self.trade_range is None:
+            if raise_error:
+                raise NotImplementedError(f"There is no trade_range in this case")
+            else:
+                return 0, day_end_idx - day_start_idx
+        else:
+            if rtype == "full":
+                val_start, val_end = self.trade_range.clip_time_range(day_start, day_end)
+            elif rtype == "step":
+                val_start, val_end = self.trade_range.clip_time_range(self.start_time, self.end_time)
+            else:
+                raise ValueError(f"This type of input {rtype} is not supported")
+            _, _, start_idx, end_index = Cal.locate_index(val_start, val_end, freq=freq)
+            return start_idx - day_start_idx, end_index - day_start_idx
+
     def empty(self) -> bool:
         for obj in self.get_decision():
             if isinstance(obj, Order):
@@ -452,9 +520,15 @@ class TradeDecisionWO(BaseTradeDecision):
     def __init__(self, order_list: List[Order], strategy: BaseStrategy, trade_range: Tuple[int, int] = None):
         super().__init__(strategy, trade_range=trade_range)
         self.order_list = order_list
+        start, end = strategy.trade_calendar.get_step_time()
+        for o in order_list:
+            if o.start_time is None:
+                o.start_time = start
+            if o.end_time is None:
+                o.end_time = end
 
     def get_decision(self) -> List[object]:
         return self.order_list
 
     def __repr__(self) -> str:
-        return f"strategy: {self.strategy}; trade_range: {self.trade_range}; order_list[{len(self.order_list)}]"
+        return f"class: {self.__class__.__name__}; strategy: {self.strategy}; trade_range: {self.trade_range}; order_list[{len(self.order_list)}]"

qlib/backtest/report.py

@@ -351,7 +351,10 @@ class Indicator:
         trade_exchange: Exchange,
         pa_config: dict = {},
     ):
-        """Get the base volume and price information"""
+        """
+        Get the base volume and price information
+        All the base price values are rooted from this function
+        """
 
         agg = pa_config.get("agg", "twap").lower()
         price = pa_config.get("price", "deal_price").lower()
@@ -374,10 +377,12 @@ class Indicator:
 
         # NOTE: there are some zeros in the trading price. These cases are known meaningless
         # for aligning the previous logic, remove it.
-        # price_s = price_s.mask(np.isclose(price_s, 0))
+        price_s = price_s[~(price_s < 1e-08)]  # remove zero and negative values.
+        # NOTE ~(price_s < 1e-08) is different from price_s >= 1e-8
 
         if agg == "vwap":
             volume_s = trade_exchange.get_volume(inst, trade_start_time, trade_end_time, method=None)
+            volume_s = volume_s.reindex(price_s.index)
         elif agg == "twap":
             volume_s = pd.Series(1, index=price_s.index)
         else:

qlib/backtest/utils.py

@@ -2,6 +2,7 @@
 # Licensed under the MIT License.
 from __future__ import annotations
 import bisect
+from qlib.utils.time import epsilon_change
 from typing import Union, TYPE_CHECKING, Tuple, Union, List, Set
 
 if TYPE_CHECKING:
@@ -22,7 +23,11 @@ class TradeCalendarManager:
     """
 
     def __init__(
-        self, freq: str, start_time: Union[str, pd.Timestamp] = None, end_time: Union[str, pd.Timestamp] = None
+        self,
+        freq: str,
+        start_time: Union[str, pd.Timestamp] = None,
+        end_time: Union[str, pd.Timestamp] = None,
+        level_infra: "LevelInfrastructure" = None,
     ):
         """
         Parameters
@@ -36,6 +41,7 @@ class TradeCalendarManager:
             closed end of the trade time range, by default None
             If `end_time` is None, it must be reset before trading.
         """
+        self.level_infra = level_infra
         self.reset(freq=freq, start_time=start_time, end_time=end_time)
 
     def reset(self, freq, start_time, end_time):
@@ -82,19 +88,19 @@ class TradeCalendarManager:
     def get_trade_step(self):
         return self.trade_step
 
-    def get_step_time(self, trade_step=0, shift=0):
+    def get_step_time(self, trade_step=None, shift=0):
         """
         Get the left and right endpoints of the trade_step'th trading interval
 
         About the endpoints:
             - Qlib uses the closed interval in time-series data selection, which has the same performance as pandas.Series.loc
-            - The returned right endpoints should minus 1 seconds becasue of the closed interval representation in Qlib.
-            Note: Qlib supports up to minutely decision execution, so 1 seconds is less than any trading time interval.
+            # - The returned right endpoints should minus 1 seconds becasue of the closed interval representation in Qlib.
+            # Note: Qlib supports up to minutely decision execution, so 1 seconds is less than any trading time interval.
 
         Parameters
         ----------
         trade_step : int, optional
-            the number of trading step finished, by default 0
+            the number of trading step finished, by default None to indicate current step
         shift : int, optional
             shift bars , by default 0
 
@@ -105,15 +111,43 @@ class TradeCalendarManager:
             - If shift > 0, return the trading time range of the earlier shift bars
             - If shift < 0, return the trading time range of the later shift bar
         """
+        if trade_step is None:
+            trade_step = self.get_trade_step()
         trade_step = trade_step - shift
         calendar_index = self.start_index + trade_step
-        return self._calendar[calendar_index], self._calendar[calendar_index + 1] - pd.Timedelta(seconds=1)
+        return self._calendar[calendar_index], epsilon_change(self._calendar[calendar_index + 1])
 
-    def get_cur_step_time(self):
+    def get_data_cal_range(self, rtype: str = "full") -> Tuple[int, int]:
         """
-        get current step time
+        get the calendar range
+        The following assumptions are made
+        1) The frequency of the exchange in common_infra is the same as the data calendar
+        2) Users want the **data index** mod by **day** (i.e. 240 min)
+
+        Parameters
+        ----------
+        rtype: str
+            - "full": return the full limitation of the deicsion in the day
+            - "step": return the limitation of current step
+
+        Returns
+        -------
+        Tuple[int, int]:
         """
-        return self.get_step_time(self.get_trade_step())
+        # potential performance issue
+        day_start = pd.Timestamp(self.start_time.date())
+        day_end = epsilon_change(day_start + pd.Timedelta(days=1))
+        freq = self.level_infra.get("common_infra").get("trade_exchange").freq
+        _, _, day_start_idx, _ = Cal.locate_index(day_start, day_end, freq=freq)
+
+        if rtype == "full":
+            _, _, start_idx, end_index = Cal.locate_index(self.start_time, self.end_time, freq=freq)
+        elif rtype == "step":
+            _, _, start_idx, end_index = Cal.locate_index(*self.get_step_time(), freq=freq)
+        else:
+            raise ValueError(f"This type of input {rtype} is not supported")
+
+        return start_idx - day_start_idx, end_index - day_start_idx
 
     def get_all_time(self):
         """Get the start_time and end_time for trading"""
@@ -147,7 +181,7 @@ class TradeCalendarManager:
         return clip(left), clip(right)
 
     def __repr__(self) -> str:
-        return f"{self.start_time}[{self.start_index}]~{self.end_time}[{self.end_index}]: [{self.trade_step}/{self.trade_len}]"
+        return f"class: {self.__class__.__name__}; {self.start_time}[{self.start_index}]~{self.end_time}[{self.end_index}]: [{self.trade_step}/{self.trade_len}]"
 
 
 class BaseInfrastructure:
@@ -198,14 +232,16 @@ class LevelInfrastructure(BaseInfrastructure):
         sub_level_infra:
         - **NOTE**: this will only work after _init_sub_trading !!!
         """
-        return ["trade_calendar", "sub_level_infra"]
+        return ["trade_calendar", "sub_level_infra", "common_infra"]
 
     def reset_cal(self, freq, start_time, end_time):
         """reset trade calendar manager"""
         if self.has("trade_calendar"):
             self.get("trade_calendar").reset(freq, start_time=start_time, end_time=end_time)
         else:
-            self.reset_infra(trade_calendar=TradeCalendarManager(freq, start_time=start_time, end_time=end_time))
+            self.reset_infra(
+                trade_calendar=TradeCalendarManager(freq, start_time=start_time, end_time=end_time, level_infra=self)
+            )
 
     def set_sub_level_infra(self, sub_level_infra: LevelInfrastructure):
         """this will make the calendar access easier when acrossing multi-levels"""

qlib/contrib/evaluate.py

@@ -73,20 +73,20 @@ def indicator_analysis(df, method="mean"):
     Parameters
     ----------
     df : pandas.DataFrame
-        columns: like ['pa', 'pos', 'ffr', 'amount', 'value'].
+        columns: like ['pa', 'pos', 'ffr', 'deal_amount', 'value'].
             Necessary fields:
                 - 'pa' is the price advantage in trade indicators
                 - 'pos' is the positive rate in trade indicators
                 - 'ffr' is the fulfill rate in trade indicators
             Optional fields:
-                - 'amount' is the total deal amount, only necessary when method is 'amount_weighted'
+                - 'deal_amount' is the total deal deal_amount, only necessary when method is 'amount_weighted'
                 - 'value' is the total trade value, only necessary when method is 'value_weighted'
 
         index: Index(datetime)
     method : str, optional
         statistics method of pa/ffr, by default "mean"
         - if method is 'mean', count the mean statistical value of each trade indicator
-        - if method is 'amount_weighted', count the amount weighted mean statistical value of each trade indicator
+        - if method is 'amount_weighted', count the deal_amount weighted mean statistical value of each trade indicator
         - if method is 'value_weighted', count the value weighted mean statistical value of each trade indicator
         Note: statistics method of pos is always "mean"
 
@@ -97,7 +97,7 @@ def indicator_analysis(df, method="mean"):
     """
     weights_dict = {
         "mean": df["count"],
-        "amount_weighted": df["amount"].abs(),
+        "amount_weighted": df["deal_amount"].abs(),
         "value_weighted": df["value"].abs(),
     }
     if method not in weights_dict:

qlib/contrib/strategy/model_strategy.py

@@ -64,7 +64,7 @@ class TopkDropoutStrategy(ModelStrategy):
 
         """
         super(TopkDropoutStrategy, self).__init__(
-            model, dataset, level_infra=level_infra, common_infra=common_infra, **kwargs
+            model, dataset, level_infra=level_infra, common_infra=common_infra, trade_exchange=trade_exchange, **kwargs
         )
         self.topk = topk
         self.n_drop = n_drop
@@ -73,22 +73,6 @@ class TopkDropoutStrategy(ModelStrategy):
         self.risk_degree = risk_degree
         self.hold_thresh = hold_thresh
         self.only_tradable = only_tradable
-        if trade_exchange is not None:
-            self.trade_exchange = trade_exchange
-
-    def reset_common_infra(self, common_infra):
-        """
-        Parameters
-        ----------
-        common_infra : dict, optional
-            common infrastructure for backtesting, by default None
-            - It should include `trade_account`, used to get position
-            - It should include `trade_exchange`, used to provide market info
-        """
-        super(TopkDropoutStrategy, self).reset_common_infra(common_infra)
-
-        if common_infra.has("trade_exchange"):
-            self.trade_exchange = common_infra.get("trade_exchange")
 
     def get_risk_degree(self, trade_step=None):
         """get_risk_degree
@@ -210,7 +194,6 @@ class TopkDropoutStrategy(ModelStrategy):
                     start_time=trade_start_time,
                     end_time=trade_end_time,
                     direction=Order.SELL,  # 0 for sell, 1 for buy
-                    factor=factor,
                 )
                 # is order executable
                 if self.trade_exchange.check_order(sell_order):
@@ -247,7 +230,6 @@ class TopkDropoutStrategy(ModelStrategy):
                 start_time=trade_start_time,
                 end_time=trade_end_time,
                 direction=Order.BUY,  # 1 for buy
-                factor=factor,
             )
             buy_order_list.append(buy_order)
         return TradeDecisionWO(sell_order_list + buy_order_list, self)
@@ -278,28 +260,12 @@ class WeightStrategyBase(ModelStrategy):
                 - In minutely execution, the daily exchange is not usable, only the minutely exchange is recommended.
         """
         super(WeightStrategyBase, self).__init__(
-            model, dataset, level_infra=level_infra, common_infra=common_infra, **kwargs
+            model, dataset, level_infra=level_infra, common_infra=common_infra, trade_exchange=trade_exchange, **kwargs
         )
         if isinstance(order_generator_cls_or_obj, type):
             self.order_generator = order_generator_cls_or_obj()
         else:
             self.order_generator = order_generator_cls_or_obj
-        if trade_exchange is not None:
-            self.trade_exchange = trade_exchange
-
-    def reset_common_infra(self, common_infra):
-        """
-        Parameters
-        ----------
-        common_infra : dict, optional
-            common infrastructure for backtesting, by default None
-            - It should include `trade_account`, used to get position
-            - It should include `trade_exchange`, used to provide market info
-        """
-        super(WeightStrategyBase, self).reset_common_infra(common_infra)
-
-        if common_infra.has("trade_exchange"):
-            self.trade_exchange = common_infra.get("trade_exchange")
 
     def get_risk_degree(self, trade_step=None):
         """get_risk_degree

qlib/contrib/strategy/rule_strategy.py

@@ -20,48 +20,6 @@ from qlib.backtest.utils import get_start_end_idx
 class TWAPStrategy(BaseStrategy):
     """TWAP Strategy for trading"""
 
-    def __init__(
-        self,
-        outer_trade_decision: BaseTradeDecision = None,
-        trade_exchange: Exchange = None,
-        level_infra: LevelInfrastructure = None,
-        common_infra: CommonInfrastructure = None,
-    ):
-        """
-        Parameters
-        ----------
-        outer_trade_decision : BaseTradeDecision
-            the trade decision of outer strategy which this startegy relies
-        trade_exchange : Exchange
-            exchange that provides market info, used to deal order and generate report
-            - If `trade_exchange` is None, self.trade_exchange will be set with common_infra
-            - It allowes different trade_exchanges is used in different executions.
-            - For example:
-                - In daily execution, both daily exchange and minutely are usable, but the daily exchange is recommended because it run faster.
-                - In minutely execution, the daily exchange is not usable, only the minutely exchange is recommended.
-
-        """
-        super(TWAPStrategy, self).__init__(
-            outer_trade_decision=outer_trade_decision, level_infra=level_infra, common_infra=common_infra
-        )
-
-        if trade_exchange is not None:
-            self.trade_exchange = trade_exchange
-
-    def reset_common_infra(self, common_infra):
-        """
-        Parameters
-        ----------
-        common_infra : CommonInfrastructure, optional
-            common infrastructure for backtesting, by default None
-            - It should include `trade_account`, used to get position
-            - It should include `trade_exchange`, used to provide market info
-        """
-        super(TWAPStrategy, self).reset_common_infra(common_infra)
-
-        if common_infra.has("trade_exchange"):
-            self.trade_exchange = common_infra.get("trade_exchange")
-
     def reset(self, outer_trade_decision: BaseTradeDecision = None, **kwargs):
         """
         Parameters
@@ -105,7 +63,9 @@ class TWAPStrategy(BaseStrategy):
                 stock_id=order.stock_id, start_time=trade_start_time, end_time=trade_end_time
             ):
                 continue
-            _amount_trade_unit = self.trade_exchange.get_amount_of_trade_unit(order.factor)
+            _amount_trade_unit = self.trade_exchange.get_amount_of_trade_unit(
+                stock_id=order.stock_id, start_time=order.start_time, end_time=order.end_time
+            )
             _order_amount = None
             # considering trade unit
             if _amount_trade_unit is None:
@@ -141,7 +101,6 @@ class TWAPStrategy(BaseStrategy):
                     start_time=trade_start_time,
                     end_time=trade_end_time,
                     direction=order.direction,  # 1 for buy
-                    factor=order.factor,
                 )
                 order_list.append(_order)
         return TradeDecisionWO(order_list=order_list, strategy=self)
@@ -161,46 +120,6 @@ class SBBStrategyBase(BaseStrategy):
     # 2. Supporting alter_outer_trade_decision
     # 3. Supporting checking the availability of trade decision
 
-    def __init__(
-        self,
-        outer_trade_decision: BaseTradeDecision = None,
-        trade_exchange: Exchange = None,
-        level_infra: LevelInfrastructure = None,
-        common_infra: CommonInfrastructure = None,
-    ):
-        """
-        Parameters
-        ----------
-        outer_trade_decision : BaseTradeDecision
-            the trade decision of outer strategy which this startegy relies
-        trade_exchange : Exchange
-            exchange that provides market info, used to deal order and generate report
-            - If `trade_exchange` is None, self.trade_exchange will be set with common_infra
-            - It allowes different trade_exchanges is used in different executions.
-            - For example:
-                - In daily execution, both daily exchange and minutely are usable, but the daily exchange is recommended because it run faster.
-                - In minutely execution, the daily exchange is not usable, only the minutely exchange is recommended.
-        """
-        super(SBBStrategyBase, self).__init__(
-            outer_trade_decision=outer_trade_decision, level_infra=level_infra, common_infra=common_infra
-        )
-
-        if trade_exchange is not None:
-            self.trade_exchange = trade_exchange
-
-    def reset_common_infra(self, common_infra):
-        """
-        Parameters
-        ----------
-        common_infra : dict, optional
-            common infrastructure for backtesting, by default None
-            - It should include `trade_account`, used to get position
-            - It should include `trade_exchange`, used to provide market info
-        """
-        super(SBBStrategyBase, self).reset_common_infra(common_infra)
-        if common_infra.has("trade_exchange"):
-            self.trade_exchange = common_infra.get("trade_exchange")
-
     def reset(self, outer_trade_decision: BaseTradeDecision = None, **kwargs):
         """
         Parameters
@@ -250,7 +169,9 @@ class SBBStrategyBase(BaseStrategy):
                     self.trade_trend[order.stock_id] = _pred_trend
                 continue
             # get amount of one trade unit
-            _amount_trade_unit = self.trade_exchange.get_amount_of_trade_unit(order.factor)
+            _amount_trade_unit = self.trade_exchange.get_amount_of_trade_unit(
+                stock_id=order.stock_id, start_time=order.start_time, end_time=order.end_time
+            )
             if _pred_trend == self.TREND_MID:
                 _order_amount = None
                 # considering trade unit
@@ -283,7 +204,6 @@ class SBBStrategyBase(BaseStrategy):
                         start_time=trade_start_time,
                         end_time=trade_end_time,
                         direction=order.direction,
-                        factor=order.factor,
                     )
                     order_list.append(_order)
 
@@ -330,7 +250,6 @@ class SBBStrategyBase(BaseStrategy):
                                 start_time=trade_start_time,
                                 end_time=trade_end_time,
                                 direction=order.direction,  # 1 for buy
-                                factor=order.factor,
                             )
                             order_list.append(_order)
                     else:
@@ -349,7 +268,6 @@ class SBBStrategyBase(BaseStrategy):
                                 start_time=trade_start_time,
                                 end_time=trade_end_time,
                                 direction=order.direction,  # 1 for buy
-                                factor=order.factor,
                             )
                             order_list.append(_order)
 
@@ -395,7 +313,9 @@ class SBBStrategyEMA(SBBStrategyBase):
         if isinstance(instruments, str):
             self.instruments = D.instruments(instruments)
         self.freq = freq
-        super(SBBStrategyEMA, self).__init__(outer_trade_decision, trade_exchange, level_infra, common_infra, **kwargs)
+        super(SBBStrategyEMA, self).__init__(
+            outer_trade_decision, level_infra, common_infra, trade_exchange=trade_exchange, **kwargs
+        )
 
     def _reset_signal(self):
         trade_len = self.trade_calendar.get_trade_len()
@@ -417,14 +337,8 @@ class SBBStrategyEMA(SBBStrategyBase):
         reset level-shared infra
         - After reset the trade calendar, the signal will be changed
         """
-        if not hasattr(self, "level_infra"):
-            self.level_infra = level_infra
-        else:
-            self.level_infra.update(level_infra)
-
-        if level_infra.has("trade_calendar"):
-            self.trade_calendar = level_infra.get("trade_calendar")
-            self._reset_signal()
+        super().reset_level_infra(level_infra)
+        self._reset_signal()
 
     def _pred_price_trend(self, stock_id, pred_start_time=None, pred_end_time=None):
         # if no signal, return mid trend
@@ -484,10 +398,9 @@ class ACStrategy(BaseStrategy):
         if isinstance(instruments, str):
             self.instruments = D.instruments(instruments)
         self.freq = freq
-        super(ACStrategy, self).__init__(outer_trade_decision, level_infra, common_infra, **kwargs)
-
-        if trade_exchange is not None:
-            self.trade_exchange = trade_exchange
+        super(ACStrategy, self).__init__(
+            outer_trade_decision, level_infra, common_infra, trade_exchange=trade_exchange, **kwargs
+        )
 
     def _reset_signal(self):
         trade_len = self.trade_calendar.get_trade_len()
@@ -506,33 +419,13 @@ class ACStrategy(BaseStrategy):
             for stock_id, stock_val in signal_df.groupby(level="instrument"):
                 self.signal[stock_id] = stock_val["volatility"].droplevel(level="instrument")
 
-    def reset_common_infra(self, common_infra):
-        """
-        Parameters
-        ----------
-        common_infra : CommonInfrastructure, optional
-            common infrastructure for backtesting, by default None
-            - It should include `trade_account`, used to get position
-            - It should include `trade_exchange`, used to provide market info
-        """
-        super(ACStrategy, self).reset_common_infra(common_infra)
-
-        if common_infra.has("trade_exchange"):
-            self.trade_exchange = common_infra.get("trade_exchange")
-
     def reset_level_infra(self, level_infra):
         """
         reset level-shared infra
         - After reset the trade calendar, the signal will be changed
         """
-        if not hasattr(self, "level_infra"):
-            self.level_infra = level_infra
-        else:
-            self.level_infra.update(level_infra)
-
-        if level_infra.has("trade_calendar"):
-            self.trade_calendar = level_infra.get("trade_calendar")
-            self._reset_signal()
+        super().reset_level_infra(level_infra)
+        self._reset_signal()
 
     def reset(self, outer_trade_decision: BaseTradeDecision = None, **kwargs):
         """
@@ -578,7 +471,9 @@ class ACStrategy(BaseStrategy):
 
             if sig_sam is None or np.isnan(sig_sam):
                 # no signal, TWAP
-                _amount_trade_unit = self.trade_exchange.get_amount_of_trade_unit(order.factor)
+                _amount_trade_unit = self.trade_exchange.get_amount_of_trade_unit(
+                    stock_id=order.stock_id, start_time=order.start_time, end_time=order.end_time
+                )
                 if _amount_trade_unit is None:
                     # divide the order into equal parts, and trade one part
                     _order_amount = self.trade_amount[order.stock_id] / (trade_len - trade_step)
@@ -599,7 +494,9 @@ class ACStrategy(BaseStrategy):
                     np.sinh(kappa * (trade_len - trade_step)) - np.sinh(kappa * (trade_len - trade_step - 1))
                 ) / np.sinh(kappa * trade_len)
                 _order_amount = order.amount * amount_ratio
-                _order_amount = self.trade_exchange.round_amount_by_trade_unit(_order_amount, order.factor)
+                _order_amount = self.trade_exchange.round_amount_by_trade_unit(
+                    _order_amount, stock_id=order.stock_id, start_time=order.start_time, end_time=order.end_time
+                )
 
             if order.direction == order.SELL:
                 # sell all amount at last
@@ -673,8 +570,6 @@ class RandomOrderStrategy(BaseStrategy):
                     .create(
                         code=stock_id,
                         amount=volume * self.volume_ratio,
-                        start_time=step_time_start,
-                        end_time=step_time_end,
                         direction=self.direction,
                     )
                 )
@@ -734,9 +629,7 @@ class FileOrderStrategy(BaseStrategy):
             execute_result will be ignored in FileOrderStrategy
         """
         oh: OrderHelper = self.common_infra.get("trade_exchange").get_order_helper()
-        tc = self.trade_calendar
-        step = tc.get_trade_step()
-        start, end = tc.get_step_time(step)
+        start, _ = self.trade_calendar.get_step_time()
         # CONVERSION: the bar is indexed by the time
         try:
             df = self.order_df.loc(axis=0)[start]
@@ -750,8 +643,6 @@ class FileOrderStrategy(BaseStrategy):
                         code=idx,
                         amount=row["amount"],
                         direction=Order.parse_dir(row["direction"]),
-                        start_time=start,
-                        end_time=end,
                     )
                 )
             return TradeDecisionWO(order_list, self, self.trade_range)

qlib/strategy/base.py

@@ -1,7 +1,8 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
+from qlib.backtest.exchange import Exchange
 from qlib.backtest.position import BasePosition
-from typing import List, Union
+from typing import List, Tuple, Union
 
 from ..model.base import BaseModel
 from ..data.dataset import DatasetH
@@ -22,6 +23,7 @@ class BaseStrategy:
         outer_trade_decision: BaseTradeDecision = None,
         level_infra: LevelInfrastructure = None,
         common_infra: CommonInfrastructure = None,
+        trade_exchange: Exchange = None,
     ):
         """
         Parameters
@@ -34,9 +36,18 @@ class BaseStrategy:
             level shared infrastructure for backtesting, including trade calendar
         common_infra : CommonInfrastructure, optional
             common infrastructure for backtesting, including trade_account, trade_exchange, .etc
+
+        trade_exchange : Exchange
+            exchange that provides market info, used to deal order and generate report
+            - If `trade_exchange` is None, self.trade_exchange will be set with common_infra
+            - It allowes different trade_exchanges is used in different executions.
+            - For example:
+                - In daily execution, both daily exchange and minutely are usable, but the daily exchange is recommended because it run faster.
+                - In minutely execution, the daily exchange is not usable, only the minutely exchange is recommended.
         """
 
-        self.reset(level_infra=level_infra, common_infra=common_infra, outer_trade_decision=outer_trade_decision)
+        self._reset(level_infra=level_infra, common_infra=common_infra, outer_trade_decision=outer_trade_decision)
+        self._trade_exchange = trade_exchange
 
     @property
     def trade_calendar(self) -> TradeCalendarManager:
@@ -46,6 +57,11 @@ class BaseStrategy:
     def trade_position(self) -> BasePosition:
         return self.common_infra.get("trade_account").current
 
+    @property
+    def trade_exchange(self) -> Exchange:
+        """get trade exchange in a prioritized order"""
+        return getattr(self, "_trade_exchange", None) or self.common_infra.get("trade_exchange")
+
     def reset_level_infra(self, level_infra: LevelInfrastructure):
         if not hasattr(self, "level_infra"):
             self.level_infra = level_infra
@@ -69,6 +85,24 @@ class BaseStrategy:
         - reset `level_infra`, used to reset trade calendar, .etc
         - reset `common_infra`, used to reset `trade_account`, `trade_exchange`, .etc
         - reset `outer_trade_decision`, used to make split decision
+
+        **NOTE**:
+        split this function into `reset` and `_reset` will make following cases more convenient
+        1. Users want to initialize his strategy by overriding `reset`, but they don't want to affect the `_reset` called
+        when initialization
+        """
+        self._reset(
+            level_infra=level_infra, common_infra=common_infra, outer_trade_decision=outer_trade_decision, **kwargs
+        )
+
+    def _reset(
+        self,
+        level_infra: LevelInfrastructure = None,
+        common_infra: CommonInfrastructure = None,
+        outer_trade_decision=None,
+    ):
+        """
+        Please refer to the docs of `reset`
         """
         if level_infra is not None:
             self.reset_level_infra(level_infra)
@@ -124,6 +158,36 @@ class BaseStrategy:
         # NOTE: normally, user should do something to the strategy due to the change of outer decision
         raise NotImplementedError(f"Please implement the `alter_outer_trade_decision` method")
 
+    # helper methods: not necessary but for convenience
+    def get_data_cal_avail_range(self, rtype: str = "full") -> Tuple[int, int]:
+        """
+        return data calendar's available decision range for `self` strategy
+        the range consider following factors
+        - data calendar in the charge of `self` strategy
+        - trading range limitation from the decision of outer strategy
+
+
+        related methods
+        - TradeCalendarManager.get_data_cal_range
+        - BaseTradeDecision.get_data_cal_range_limit
+
+        Parameters
+        ----------
+        rtype: str
+            - "full": return the available data index range of the strategy from `start_time` to `end_time`
+            - "step": return the available data index range of the strategy of current step
+
+        Returns
+        -------
+        Tuple[int, int]:
+            the available range both sides are closed
+        """
+        cal_range = self.trade_calendar.get_data_cal_range(rtype=rtype)
+        if self.outer_trade_decision is None:
+            raise ValueError(f"There is not limitation for strategy {self}")
+        range_limit = self.outer_trade_decision.get_data_cal_range_limit(rtype=rtype)
+        return max(cal_range[0], range_limit[0]), min(cal_range[1], range_limit[1])
+
 
 class ModelStrategy(BaseStrategy):
     """Model-based trading strategy, use model to make predictions for trading"""

qlib/utils/__init__.py

@@ -210,10 +210,13 @@ def get_cls_kwargs(config: Union[dict, str], default_module: Union[str, ModuleTy
         the class object and it's arguments.
     """
     if isinstance(config, dict):
-        module = get_module_by_module_path(config.get("module_path", default_module))
+        if isinstance(config["class"], str):
+            module = get_module_by_module_path(config.get("module_path", default_module))
 
-        # raise AttributeError
-        klass = getattr(module, config["class"])
+            # raise AttributeError
+            klass = getattr(module, config["class"])
+        else:
+            klass = config["class"]  # the class type itself is passed in
         kwargs = config.get("kwargs", {})
     elif isinstance(config, str):
         module = get_module_by_module_path(default_module)
@@ -235,11 +238,17 @@ def init_instance_by_config(
     ----------
     config : Union[str, dict, object]
         dict example.
+            case 1)
             {
                 'class': 'ClassName',
                 'kwargs': dict, #  It is optional. {} will be used if not given
                 'model_path': path, # It is optional if module is given
             }
+            case 2)
+            {
+                'class': <The class it self>,
+                'kwargs': dict, #  It is optional. {} will be used if not given
+            }
         str example.
             1) specify a pickle object
                 - path like 'file:///<path to pickle file>/obj.pkl'

qlib/utils/time.py

@@ -160,5 +160,32 @@ def cal_sam_minute(x: pd.Timestamp, sam_minutes: int) -> pd.Timestamp:
     return concat_date_time(date, new_time)
 
 
+def epsilon_change(datetime: pd.Timestamp, direction: str = "backward") -> pd.Timestamp:
+    """
+    change the time by infinitely small quantity.
+
+
+    Parameters
+    ----------
+    datetime : pd.Timestamp
+        the original time
+    direction : str
+        the direction the time are going to
+        - "backward" for going to history
+        - "forward" for going to the future
+
+    Returns
+    -------
+    pd.Timestamp:
+        the shifted time
+    """
+    if direction == "backward":
+        return datetime - pd.Timedelta(seconds=1)
+    elif direction == "forward":
+        return datetime + pd.Timedelta(seconds=1)
+    else:
+        raise ValueError("Wrong input")
+
+
 if __name__ == "__main__":
     print(get_day_min_idx_range("8:30", "14:59", "10min"))